Foreign direct investment, environmental regulation, and energy transition—An empirical study based on data from 38 OECD countries worldwide
Bibliographic record
Abstract
Abstract This study formulates a theoretical hypothesis regarding the intricate interplay among foreign direct investment (FDI), environmental regulation, and energy transition. To empirically validate this hypothesis, a comprehensive analysis is carried out on 38 member nations of the Organization for Economic Cooperation and Development (OECD) over the span of 2003 to 2020, utilizing a panel fixed effects model, a panel quantile model, and a panel threshold regression model. The findings of the research indicate that (1) FDI exhibits an inhibitory impact on energy transition and, according to the heterogeneity analysis, FDI significantly inhibits energy transition in developed nations. However, the inhibitory influence on energy transition in developing nations is not as pronounced. (2) A considerable amount of dampening influence is exerted by FDI on energy transition at various stages of the transition and is most apparent when the transition is in the growth phase. (3) A threshold effect is evident in FDI and environmental regulation in relation to energy transition owing to the mismatch between the two developments. Notably, the influence of FDI on energy transition significantly varies based on the stringency of environmental regulation. As formal environmental regulation surpasses a designated threshold, FDI shifted from facilitating to inhibiting the energy transition. The same conclusions were reached when informal environmental regulation was considered as the threshold variable. Drawing from the conclusions of this paper, the countries of the OECD can develop a theoretical framework to formulate foreign investment introduction policies and regulate environmental regulation efforts to promote the energy transition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".